Official Code of "ADPal: Automatic Detection of Troubled Users in Online Service Systems via Page Access Logs".
Install with pip install -r requirements.txt.
Or follow instructions in requirements.txt to install.
训练的时候，主要可调的参数有max_seq_len, embedding_dim, hidden_dim, steps_per_epoch, batch_size
# 第一次运行时生成cache文件，注意！！！更改max_seq_len之后需要去掉--cache重新运行一遍
# 之后的运行可以指定cache参数。
python train.py -dataset_root=/home/hiyoungshen/Source/ICWS2023/AbnormalDetection/experiment/preprocess/ \
-cache_dir /home/hiyoungshen/Source/ICWS2023/AbnormalDetection/experiment/AirExperimentOutput.1/after_newtrain_data \
-weight_name model.pkl \
-file_name_abnormal feedback.csv \
-file_name_normal normal.csv \
--use_cache \
-data_type pageuser \
-max_seq_len 300 \
-vocab_dict_path experiment/assets/page2idx.json \
-vocab_size 10000 \
-backbone lstma \
-embedding_dim 280 \
-hidden_dim 200 \
-criterion WeightedBCE \
-weight 0.01 0.99 \
-sampler balanced \
-dropout 0.5 \
-lr 0.0002 \
-epochs 3 \
-steps_per_epoch 40 \
-batch_size 128 \
-train_ratio 0.8
# 可以使用的loss有BCE, focal, deviation，但是感觉BCE就够了
python test.py -dataset_root=/home/hiyoungshen/Source/ICWS2023/AbnormalDetection/experiment/preprocess/ \
-cache_dir /home/hiyoungshen/Source/ICWS2023/AbnormalDetection/experiment/AirExperimentOutput.1/after_newtest_data \
-weight_name model.pkl \
-file_name_abnormal feedback.csv \
-file_name_normal normal.csv \
--use_cache \
-test_set train \
-data_type pageuser \
-max_seq_len 300 \
-vocab_dict_path experiment/assets/page2idx.json \
-vocab_size 10000 \
-backbone lstma \
-embedding_dim 280 \
-hidden_dim 200 \
-criterion WeightedBCE \
-weight 0.01 0.99 \
-sampler balanced \
-dropout 0.5 \
-lr 0.0002 \
-epochs 3 \
-steps_per_epoch 40 \
-batch_size 128 \
-train_ratio 0.8transformer为backbone。
训练的时候，主要可调的参数有max_seq_len, embedding_dim, ffn_num_hiddens, num_heads, num_layers, batch_size。
注意：embedding_dim必须是num_heads的倍数
# 第一次运行时生成cache文件，注意！！！更改max_seq_len之后需要去掉--cache重新运行一遍
python train.py -dataset_root=/home/hiyoungshen/Source/ICWS2023/AbnormalDetection/experiment/preprocess/ \
-weight_name model.pkl \
-file_name_abnormal feedback.csv \
-file_name_normal normal.csv \
-max_seq_len 200 \
-vocab_dict_path experiment/assets/page2idx.json \
-vocab_size 10000 \
-backbone transformer \
-embedding_dim 280 \
-ffn_num_hiddens 200 \
-num_heads 4 \
-num_layers 2 \
-dropout 0.5 \
-criterion BCE \
-weight 0.9 0.1 \
-lr 0.0002 \
-epochs 30 \
-steps_per_epoch 40 \
-batch_size 128 \
-train_ratio 0.8
# 之后的运行可以指定cache参数。
python train.py -dataset_root=/home/hiyoungshen/Source/ICWS2023/AbnormalDetection/experiment/preprocess/ \
-weight_name model.pkl \
-file_name_abnormal feedback.csv \
-file_name_normal normal.csv \
--use_cache \
-max_seq_len 200 \
-vocab_dict_path experiment/assets/page2idx.json \
-vocab_size 10000 \
-backbone transformer \
-embedding_dim 280 \
-ffn_num_hiddens 200 \
-num_heads 4 \
-num_layers 2 \
-dropout 0.5 \
-criterion BCE \
-weight 0.9 0.1 \
-lr 0.0002 \
-epochs 30 \
-steps_per_epoch 40 \
-batch_size 128 \
-train_ratio 0.8只要加上-data_type user即可(最好调一下max_seq_len参数调大一些，当然也不是说这么做就好，只是直觉上感觉，具体这么做有没有效果，调参决定)，注意！！！更改max_seq_len和data_type之后都需要去掉--cache重新运行一遍。
第一次运行不加上--use_cache选项以生成cache，之后加上--use_cache选项。
python train.py -dataset_root=/home/hiyoungshen/Source/ICWS2023/AbnormalDetection/experiment/preprocess/ \
-cache_dir /home/hiyoungshen/Source/ICWS2023/AbnormalDetection/experiment/AirExperimentOutput.1/after_newtrain_data \
-weight_name model.pkl \
-file_name_abnormal feedback.csv \
-file_name_normal normal.csv \
--use_cache \
-data_type pageuser \
-max_seq_len 300 \
-vocab_dict_path experiment/assets/page2idx.json \
-vocab_size 10000 \
-backbone transformer \
-embedding_dim 360 \
-ffn_num_hiddens 1440 \
-num_heads 4 \
-num_layers 2 \
-dropout 0.5 \
-criterion WeightedBCE \
-weight 0.01 0.99 \
-sampler balanced \
-lr 0.0002 \
-epochs 2 \
-steps_per_epoch 40 \
-batch_size 128 \
-train_ratio 0.8criterion：Contrastive, WeightedBCE, BCE, deviation, focal sampler: randomed, balanced, balanced_step
测试
python test.py -dataset_root=/home/hiyoungshen/Source/ICWS2023/AbnormalDetection/experiment/preprocess/ \
-cache_dir /home/hiyoungshen/Source/ICWS2023/AbnormalDetection/experiment/AirExperimentOutput.1/after_newtest_data \
-weight_name model.pkl \
-file_name_abnormal feedback.csv \
-file_name_normal normal.csv \
--use_cache \
-test_set train \
-data_type pageuser \
-max_seq_len 300 \
-vocab_dict_path experiment/assets/page2idx.json \
-vocab_size 10000 \
-backbone transformer \
-embedding_dim 360 \
-ffn_num_hiddens 1440 \
-num_heads 4 \
-num_layers 2 \
-dropout 0.5 \
-criterion WeightedBCE \
-weight 0.01 0.99 \
-sampler balanced \
-lr 0.0002 \
-epochs 2 \
-steps_per_epoch 40 \
-batch_size 128 \
-train_ratio 0.8第一次运行不加上--use_cache选项以生成cache，之后加上--use_cache选项。
python train.py -dataset_root=/home/hiyoungshen/Source/ICWS2023/AbnormalDetection/experiment/preprocess/ \
-weight_name model.pkl \
-file_name_abnormal feedback.csv \
-file_name_normal normal.csv \
--use_cache \
-data_type worduser \
-max_seq_len 400 \
-vocab_dict_path experiment/assets/word2idx.json \
-vocab_size 10000 \
-backbone transformer \
-embedding_dim 512 \
-ffn_num_hiddens 400 \
-num_heads 4 \
-num_layers 2 \
-dropout 0.5 \
-criterion BCE \
-weight 0.9 0.1 \
-lr 0.0002 \
-epochs 30 \
-steps_per_epoch 40 \
-batch_size 128 \
-train_ratio 0.8注意！！！用什么样子的参数训练，就要用什么样子的参数测试。
python test.py -dataset_root=/home/hiyoungshen/Source/ICWS2023/AbnormalDetection/experiment/preprocess/ \
-weight_name model.pkl \
-file_name_abnormal feedback.csv \
-file_name_normal normal.csv \
--use_cache \
-max_seq_len 300 \
-vocab_dict_path experiment/assets/page2idx.json \
-vocab_size 10000 \
-backbone transformer \
-embedding_dim 380 \
-ffn_num_hiddens 300 \
-num_heads 4 \
-num_layers 2 \
-dropout 0.5 \
-criterion BCE \
-weight 0.9 0.1 \
-lr 0.0002 \
-epochs 30 \
-steps_per_epoch 40 \
-batch_size 128 \
-train_ratio 0.8"Explainable Deep Few-shot Anomaly Detection with Deviation Networks"
React
Typescript
Django
Laravel
Facebook
Microsoft
Google
Alibaba
D3
Tencent